Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics

Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling. For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022). [Display omitted] •A computational framework for patient-specific modeling•Integration of clinical data and cell line data for model calibration•Building a mechanistic dynamic model from .txt file•Stratification of patients with breast cancer based on in silico signaling dynamics Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling.